Evaluation of Penetration Depth and Caries Progression using Resin Infiltrant in Natural White Spot Lesions of Primary Molars.

Background: Management of white spot lesions (WSLs) using resin infiltrants, like ICON®, is a novel technique that emphasizes the concept of minimally invasive dentistry. Aim: Evaluate penetration depth and its effect on caries progression of ICON® in natural WSLs of primary molars. Design: A total of 30 human primary molars with natural WSLs were selected. A total of 15 samples each were randomly selected to evaluate penetration depth and caries progression. To evaluate penetration depth, lesion surfaces were sectioned into two halves, wherein one half served as control, while the other half received ICON® infiltration treatment. To evaluate the effect on caries progression, samples were sectioned into two halves, wherein one half served as a control to its experimental counterpart, which received ICON® infiltration. The initial depth of the subsurface lesion (baseline data) was measured, and all the sample subsets underwent pH cycling. Confocal laser fluorescence microscope (CFLM) evaluation was performed, and penetration depth was assessed using a laser scanning microscopy (LSM) image reader, while caries progression was evaluated using PhotoScape® software. Results: ICON® resin infiltrant penetrated to significant depths into the WSLs (p < 0.001) and demonstrated significant inhibition of caries progression (p < 0.001). Conclusion: The novel technique using ICON® could be an invaluable tool in the management of WSLs in primary teeth. How to cite this article: Babu G, Mallikarjun S, Vijayan V, et al. Evaluation of Penetration Depth and Caries Progression using Resin Infiltrant in Natural White Spot Lesions of Primary Molars. Int J Clin Pediatr Dent 2024;17(4):390-394.

Nanoscale single-vesicle analysis: High-throughput approaches through AI-enhanced super-resolution image analysis.

The analysis of membrane vesicles at the nanoscale level is crucial for advancing the understanding of intercellular communication and its implications for health and disease. Despite their significance, the nanoscale analysis of vesicles at the single particle level faces challenges owing to their small size and the complexity of biological fluids. This new vesicle analysis tool leverages the single-molecule sensitivity of super-resolution microscopy (SRM) and the high-throughput analysis capability of deep-learning algorithms. By comparing classical clustering methods (k-means, DBSCAN, and SR-Tesseler) with deep-learning-based approaches (YOLO, DETR, Deformable DETR, and Faster R-CNN) for the analysis of super-resolution fluorescence images of exosomes, we identified the deep-learning algorithm, Deformable DETR, as the most effective. It showed superior accuracy and a reduced processing time for detecting individual vesicles from SRM images. Our findings demonstrate that image-based deep-learning-enhanced methods from SRM images significantly outperform traditional coordinate-based clustering techniques in identifying individual vesicles and resolving the challenges related to misidentification and computational demands. Moreover, the application of the combined Deformable DETR and ConvNeXt-S algorithms to differently labeled exosomes revealed its capability to differentiate between them, indicating its potential to dissect the heterogeneity of vesicle populations. This breakthrough in vesicle analysis suggests a paradigm shift towards the integration of AI into super-resolution imaging, which is promising for unlocking new frontiers in vesicle biology, disease diagnostics, and the development of vesicle-based therapeutics.

Benchmarking robustness of deep neural networks in semantic segmentation of fluorescence microscopy images.

BACKGROUND: Fluorescence microscopy (FM) is an important and widely adopted biological imaging technique. Segmentation is often the first step in quantitative analysis of FM images. Deep neural networks (DNNs) have become the state-of-the-art tools for image segmentation. However, their performance on natural images may collapse under certain image corruptions or adversarial attacks. This poses real risks to their deployment in real-world applications. Although the robustness of DNN models in segmenting natural images has been studied extensively, their robustness in segmenting FM images remains poorly understood RESULTS: To address this deficiency, we have developed an assay that benchmarks robustness of DNN segmentation models using datasets of realistic synthetic 2D FM images with precisely controlled corruptions or adversarial attacks. Using this assay, we have benchmarked robustness of ten representative models such as DeepLab and Vision Transformer. We find that models with good robustness on natural images may perform poorly on FM images. We also find new robustness properties of DNN models and new connections between their corruption robustness and adversarial robustness. To further assess the robustness of the selected models, we have also benchmarked them on real microscopy images of different modalities without using simulated degradation. The results are consistent with those obtained on the realistic synthetic images, confirming the fidelity and reliability of our image synthesis method as well as the effectiveness of our assay. CONCLUSIONS: Based on comprehensive benchmarking experiments, we have found distinct robustness properties of deep neural networks in semantic segmentation of FM images. Based on the findings, we have made specific recommendations on selection and design of robust models for FM image segmentation.

Tumor spheroid elasticity estimation using mechano-microscopy combined with a conditional generative adversarial network.

BACKGROUND AND OBJECTIVES: Techniques for imaging the mechanical properties of cells are needed to study how cell mechanics influence cell function and disease progression. Mechano-microscopy (a high-resolution variant of compression optical coherence elastography) generates elasticity images of a sample undergoing compression from the phase difference between optical coherence microscopy (OCM) B-scans. However, the existing mechano-microscopy signal processing chain (referred to as the algebraic method) assumes the sample stress is uniaxial and axially uniform, such that violation of these assumptions reduces the accuracy and precision of elasticity images. Furthermore, it does not account for prior information regarding the sample geometry or mechanical property distribution. In this study, we investigate the feasibility of training a conditional generative adversarial network (cGAN) to generate elasticity images from phase difference images of samples containing a cell spheroid embedded in a hydrogel. METHODS: To construct the cGAN training and simulated test sets, we generated 30,000 artificial elasticity images using a parametric model and computed the corresponding phase difference images using finite element analysis to simulate compression applied to the artificial samples. We also imaged real MCF7 breast tumor spheroids embedded in hydrogel using mechano-microscopy to construct the experimental test set and evaluated the cGAN using the algebraic elasticity images and co-registered OCM and confocal fluorescence microscopy (CFM) images. RESULTS: Comparison with the simulated test set ground truth elasticity images shows the cGAN produces a lower root mean square error (median: 3.47 kPa, 95 % confidence interval (CI) [3.41, 3.52]) than the algebraic method (median: 4.91 kPa, 95 % CI [4.85, 4.97]). For the experimental test set, the cGAN elasticity images contain features resembling stiff nuclei at locations corresponding to nuclei seen in the algebraic elasticity, OCM, and CFM images. Furthermore, the cGAN elasticity images are higher resolution and more robust to noise than the algebraic elasticity images. CONCLUSIONS: The cGAN elasticity images exhibit better accuracy, spatial resolution, sensitivity, and robustness to noise than the algebraic elasticity images for both simulated and real experimental data.

Bright New Resources for Syphilis Research: Genetically Encoded Fluorescent Tags for Treponema pallidum and Sf1Ep Cells.

The recently discovered methodologies to cultivate and genetically manipulate Treponema pallidum subsp. pallidum (T. pallidum) have significantly helped syphilis research, allowing the in vitro evaluation of antibiotic efficacy, performance of controlled studies to assess differential treponemal gene expression, and generation of loss-of-function mutants to evaluate the contribution of specific genetic loci to T. pallidum virulence. Building on this progress, we engineered the T. pallidum SS14 strain to express a red-shifted green fluorescent protein (GFP) and Sf1Ep cells to express mCherry and blue fluorescent protein (BFP) for enhanced visualization. These new resources improve microscopy- and cell sorting-based applications for T. pallidum, better capturing the physical interaction between the host and pathogen, among other possibilities. Continued efforts to develop and share new tools and resources are required to help our overall knowledge of T. pallidum biology and syphilis pathogenesis reach that of other bacterial pathogens, including spirochetes.

Five near-infrared-emissive graphene quantum dots for multiplex bioimaging.

Due to high tissue penetration depth and low autofluorescence backgrounds, near-infrared (NIR) fluorescence imaging has recently become an advantageous diagnostic technique used in a variety of fields. However, most of the NIR fluorophores do not have therapeutic delivery capabilities, exhibit low photostabilities, and raise toxicity concerns. To address these issues, we developed and tested five types of biocompatible graphene quantum dots (GQDs) exhibiting spectrally-separated fluorescence in the NIR range of 928-1053 nm with NIR excitation. Their optical properties in the NIR are attributed to either rare-earth metal dopants (Ho-NGQDs, Yb-NGQDs, Nd-NGQDs) or defect-states (nitrogen doped GQDS (NGQDs), reduced graphene oxides) as verified by Hartree-Fock calculations. Moderate up to 1.34% quantum yields of these GQDs are well-compensated by their remarkable >4 h photostability. At the biocompatible concentrations of up to 0.5-2 mg ml-1 GQDs successfully internalize into HEK-293 cells and enable in vitro imaging in the visible and NIR. Tested all together in HEK-293 cells five GQD types enable simultaneous multiplex imaging in the NIR-I and NIR-II shown for the first time in this work for GQD platforms. Substantial photostability, spectrally-separated NIR emission, and high biocompatibility of five GQD types developed here suggest their promising potential in multianalyte testing and multiwavelength bioimaging of combination therapies.

DNA Origami - Lipid Membrane Interactions Controlled by Nanoscale Sterics.

DNA nanostructures designed to interact with bilayer membranes are of fundamental interest as they mimic biological cytoskeletons and other membrane-associated proteins for applications in synthetic biology, biosensing, and biological research. Yet, there is limited insight into how the binary interactions are influenced by steric effects produced by 3D geometries of DNA structures and membranes. This work uses a 3D DNA nanostructure with membrane anchors in four different steric environments to elucidate the interaction with membrane vesicles of varying sizes and different local bilayer morphology. It is found that interactions are significantly affected by the steric environments of the anchors -often against predicted accessibility- as well as local nanoscale morphology of bilayers rather than on the usually considered global vesicle size. Furthermore, anchor-mediated bilayer interactions are co-controlled by weak contacts with non-lipidated DNA regions, as showcased by pioneering size discrimination between 50 and 200 nm vesicles. This study extends DNA nanotechnology to controlled bilayer interactions and can facilitate the design of nanodevices for vesicle-based diagnostics, biosensing, and protocells.

Hybrid Amyloid Quantum Dot Nano-Bio Assemblies to Probe Neuroinflammatory Damage.

Various oligomeric species of amyloid-beta have been proposed to play different immunogenic roles in the cellular pathology of Alzheimer's Disease. The dynamic interconversion between various amyloid oligomers and fibrillar assemblies makes it difficult to elucidate the role each potential aggregation state may play in driving neuroinflammatory and neurodegenerative pathology. The ability to identify the amyloid species that are key and essential drivers of these pathological hallmarks of Alzheimer's Disease is of fundamental importance for also understanding downstream events including tauopathies that mediate neuroinflammation with neurologic deficits. Here, we report the design and construction of a quantum dot mimetic for larger spherical oligomeric amyloid species as an "endogenously" fluorescent proxy for this cytotoxic assembly of amyloid to investigate its role in inducing inflammatory and stress response states in neuronal and glial cell types. The design parameters and construction protocol developed here may be adapted for developing quantum dot nano-bio assemblies for other biological systems of interest, particularly neurodegenerative diseases involving other protein aggregates.

Development of a Dual Reporter System to Simultaneously Visualize Ca2+ Signals and AMPK Activity.

In this study, we introduce a new separation of phases-based activity reporter of kinase (SPARK) for AMP-activated kinase (AMPK), named AMPK-SPARK, which reports the AMPK activation by forming bright fluorescent clusters. Furthermore, we introduce a dual reporter system, named GCaMP-AMPK-SPARK, by incorporating a single-fluorescent protein (FP)-based Ca2+ biosensor, GCaMP6f, into our initial design, enabling simultaneous monitoring of Ca2+ levels and AMPK activity. This system offers the essential quality of information by single-channel fluorescence microscopy without the need for coexpression of different biosensors and elaborate filter layouts to overcome spectral limitations. We used AMPK-SPARK to map endogenous AMPK activity in different cell types and visualized the dynamics of AMPK activation in response to various stimuli. Using GCaMP-AMPK-SPARK, we revealed cell-to-cell heterogeneities in AMPK activation by Ca2+ mobilization. We anticipate that this dual reporter strategy can be employed to study the intricate interplays between different signaling networks and kinase activities.

Methods to Monitor Nucleophagy in Yeast.

The selective degradation of nuclear components via autophagy, termed nucleophagy, is an essential process observed from yeasts to mammals and crucial for maintaining nucleus homeostasis and regulating nucleus functions. In the budding yeast Saccharomyces cerevisiae, nucleophagy occurs in two different manners: one involves autophagosome formation for the sequestration and vacuolar transport of nucleus-derived vesicles (NDVs), and the other proceeds with the invagination of the vacuolar membrane for the uptake of NDVs into the vacuole, termed macronucleophagy and micronucleophagy, respectively. This chapter describes methods to analyze and quantify activities of these nucleophagy pathways in yeast.

Fluorescence Microscopy and Immunoblotting for Mitophagy in Budding Yeast.

Selective removal of excess or damaged mitochondria is an evolutionarily conserved process that contributes to mitochondrial quality and quantity control. This catabolic event relies on autophagy, a membrane trafficking system that sequesters cytoplasmic constituents into double membrane-bound autophagosomes and delivers them to lysosomes (vacuoles in yeast) for hydrolytic degradation and is thus termed mitophagy. Dysregulation of mitophagy is associated with various diseases, highlighting its physiological relevance. In budding yeast, the pro-mitophagic single-pass membrane protein Atg32 is upregulated under prolonged respiration or nutrient starvation, anchored on the surface of mitochondria, and activated to recruit the autophagy machinery for the formation of autophagosomes surrounding mitochondria. In this chapter, we provide protocols to assess Atg32-mediated mitophagy using fluorescence microscopy and immunoblotting.

Fluorescence Methods to Measure Pexophagy.

We outline our approach for studying the selective autophagy of peroxisomes (pexophagy), using fluorescence microscopy in tissue cell culture models. Ratiometric reporters, which specifically localize to peroxisomes, allow a quantitative assessment of pexophagy in fixed and live cells, as well as whole organisms. We discuss chemical and physiological inducers of pexophagy and any overlap with the induction of mitophagy.

In Ovo Electroporation of Embryonic Chicken Spinal Cord in Combination with Ex Vivo Slice Culture for Live Imaging of Vertebrate Neuronal Differentiation.

To investigate the cell behavior underlying neuronal differentiation in a physiologically relevant context, differentiating neurons must be studied in their native tissue environment. Here, we describe an accessible protocol for fluorescent live imaging of differentiating neurons within ex vivo embryonic chicken spinal cord slice cultures, which facilitates long-term observation of individual cells within developing tissue.

A Live Imaging Assay for Regenerating Peripheral Neurons.

The cell intrinsic mechanisms directing peripheral nerve regeneration have remained largely understudied, thus limiting our understanding of these processes and constraining the advancement of novel clinical therapeutics. The use of primary adult rat dorsal root ganglion (DRG) neurons cultured in vitro is well established. Despite this, these cells can be challenging to culture and have so far not been amenable to robust transfection or live-cell imaging. The ability to transfect these cells with fluorescent plasmid constructs to label subcellular structures, combined with high resolution time-lapse imaging has the potential to provide invaluable insight into how peripheral neurons coordinate their regenerative response, and which specific cellular structures are involved in this process. Here we describe a protocol that facilitates transfection and subsequent live-imaging of adult rat DRG neurons.

(1-Deoxy)ceramides in bilayers containing sphingomyelin and cholesterol.

The discovery of a novel sphingolipid subclass, the (1-deoxy)sphingolipids, which lack the 1-hydroxy group, attracted considerable attention in the last decade, mainly due to their involvement in disease. They differed in their physico-chemical properties from the canonical (or 1-hydroxy) sphingolipids and they were more toxic when accumulated in cells, inducing neurodegeneration and other dysfunctions. (1-Deoxy)ceramides, (1-deoxy)dihydroceramides, and (1- deoxymethyl)dihydroceramides, the latter two containing a saturated sphingoid chain, have been studied in this work using differential scanning calorimetry, confocal fluorescence and atomic force microscopy, to evaluate their behavior in bilayers composed of mixtures of three or four lipids. When compared to canonical ceramides (Cer), a C16:0 (1-deoxy)Cer shows a lower miscibility in mixtures of the kind C16:0 sphingomyelin/cholesterol/XCer, where XCer is any (1-deoxy)ceramide, giving rise to the coexistence of a liquid-ordered phase and a gel phase. The latter resembles, in terms of thermotropic behavior and nanomechanical resistance, the gel phase of the C16:0 sphingomyelin/cholesterol/C16:0 Cer mixture [Busto et al., Biophys. J. 2014, 106, 621-630]. Differences are seen between the various C16:0 XCer under study in terms of nanomechanical resistance, bilayer thickness and bilayer topography. When examined in a more fluid environment (bilayers based on C24:1 SM), segregated gel phases are still present. Probably related to such lateral separation, XCer preserve the capacity for membrane permeation, but their effects are significantly lower than those of canonical ceramides. Moreover, C24:1 XCer show significantly lower membrane permeation capacity than their C16:0 counterparts. The above data may be relevant in the pathogenesis of certain sphingolipid-related diseases, including certain neuropathies, diabetes, and glycogen storage diseases.

Circadian period is compensated for repressor protein turnover rates in single cells.

Most mammalian cells have molecular circadian clocks that generate widespread rhythms in transcript and protein abundance. While circadian clocks are robust to fluctuations in the cellular environment, little is known about the mechanisms by which the circadian period compensates for fluctuating metabolic states. Here, we exploit the heterogeneity of single cells both in circadian period and a metabolic parameter-protein stability-to study their interdependence without the need for genetic manipulation. We generated cells expressing key circadian proteins (CRYPTOCHROME1/2 (CRY1/2) and PERIOD1/2 (PER1/2)) as endogenous fusions with fluorescent proteins and simultaneously monitored circadian rhythms and degradation in thousands of single cells. We found that the circadian period compensates for fluctuations in the turnover rates of circadian repressor proteins and uncovered possible mechanisms using a mathematical model. In addition, the stabilities of the repressor proteins are circadian phase dependent and correlate with the circadian period in a phase-dependent manner, in contrast to the prevailing model.

Dynamic Live Cell Imaging of Budding Yeast Meiosis.

For over a century, major advances in understanding meiosis have come from the use of microscopy-based methods. Studies using the budding yeast, Saccharomyces cerevisiae, have made important contributions to our understanding of meiosis because of the facility with which budding yeast can be manipulated as a genetic model organism. In contrast, imaging-based approaches with budding yeast have been constrained by the small size of its chromosomes. The advent of advances in fluorescent chromosome tagging techniques has made it possible to use yeast more effectively for imaging-based approaches as well. This protocol describes live cell imaging methods that can be used to monitor chromosome movements throughout meiosis in living yeast cells.

Assessing Intra-Bundle Impregnation in Partially Impregnated Glass Fiber-Reinforced Polypropylene Composites Using a 2D Extended-Field and Multimodal Imaging Approach.

This study evaluates multimodal imaging for characterizing microstructures in partially impregnated thermoplastic matrix composites made of woven glass fiber and polypropylene. The research quantifies the impregnation degree of fiber bundles within composite plates manufactured through a simplified compression resin transfer molding process. For comparison, a reference plate was produced using compression molding of film stacks. An original surface polishing procedure was introduced to minimize surface defects while polishing partially impregnated samples. Extended-field 2D imaging techniques, including polarized light, fluorescence, and scanning electron microscopies, were used to generate images of the same microstructure at fiber-scale resolutions throughout the plate. Post-processing workflows at the macro-scale involved stitching, rigid registration, and pixel classification of FM and SEM images. Meso-scale workflows focused on 0°-oriented fiber bundles extracted from extended-field images to conduct quantitative analyses of glass fiber and porosity area fractions. A one-way ANOVA analysis confirmed the reliability of the statistical data within the 95% confidence interval. Porosity quantification based on the conducted multimodal approach indicated the sensitivity of the impregnation degree according to the layer distance from the pool of melted polypropylene in the context of simplified-CRTM. The findings underscore the potential of multimodal imaging for quantitative analysis in composite material production.

Placental Element Content Assessed via Synchrotron-Based X-ray Fluorescence Microscopy Identifies Low Molybdenum Concentrations in Foetal Growth Restriction, Postdate Delivery and Stillbirth.

Placental health and foetal development are dependent upon element homeostasis. Analytical techniques such as mass spectroscopy can provide quantitative data on element concentrations in placental tissue but do not show spatial distribution or co-localisation of elements that may affect placental function. The present study used synchrotron-based X-ray fluorescence microscopy to elucidate element content and distribution in healthy and pathological placental tissue. The X-ray fluorescence microscopy (XFM) beamline at the Australian Synchrotron was used to image trace metal content of 19 placental sections from healthy term (n = 5, 37-39 weeks), foetal growth-restricted (n = 3, <32 weeks, birth weight <3rd centile), postdate (n = 7, >41 completed weeks), and stillbirth-complicated pregnancies (n = 4, 37-40 weeks). Samples were cryo-sectioned and freeze-dried. The concentration and distribution of fourteen elements were detected in all samples: arsenic, bromine, calcium, chlorine, copper, iron, molybdenum, phosphorous, potassium, rubidium, selenium, strontium, sulphur, and zinc. The elements zinc, calcium, phosphorous, and strontium were significantly increased in stillbirth placental tissue in comparison to healthy-term controls. Strontium, zinc, and calcium were found to co-localise in stillbirth tissue samples, and calcium and strontium concentrations were correlated in all placental groups. Molybdenum was significantly decreased in stillbirth, foetal growth-restricted, and postdate placental tissue in comparison to healthy-term samples (p < 0.0001). Synchrotron-based XFM reveals elemental distribution within biological samples such as the placenta, allowing for the co-localisation of metal deposits that may have a pathological role. Our pilot study further indicates low concentrations of placental molybdenum in pregnancies complicated by foetal growth restriction, postdate delivery, and stillbirth.

Posterior approach to correct for focal plane offsets in lattice light-sheet structured illumination microscopy.

Significance: Lattice light-sheet structured illumination microscopy (latticeSIM) has proven highly effective in producing three-dimensional images with super resolution rapidly and with minimal photobleaching. However, due to the use of two separate objectives, sample-induced aberrations can result in an offset between the planes of excitation and detection, causing artifacts in the reconstructed images. Aim: We introduce a posterior approach to detect and correct the axial offset between the excitation and detection focal planes in latticeSIM and provide a method to minimize artifacts in the reconstructed images. Approach: We utilized the residual phase information within the overlap regions of the laterally shifted structured illumination microscopy information components in frequency space to retrieve the axial offset between the excitation and the detection focal planes in latticeSIM. Results: We validated our technique through simulations and experiments, encompassing a range of samples from fluorescent beads to subcellular structures of adherent cells. We also show that using transfer functions with the same axial offset as the one present during data acquisition results in reconstructed images with minimal artifacts and salvages otherwise unusable data. Conclusion: We envision that our method will be a valuable addition to restore image quality in latticeSIM datasets even for those acquired under non-ideal experimental conditions.